Precision cardiodiet: transforming cardiac care with artificial intelligence-driven dietary recommendations

Abstract

The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from extensive datasets. This approach involves creating a sophisticated diet recommendation framework using diverse ML techniques. These techniques are meticulously applied to analyze data and identify optimal dietary choices for individuals with cardiac concerns. In pursuit of actionable dietary recommendations, classification algorithms are employed instead of clustering. These algorithms categorize foods as "heart-healthy" or "not heart-healthy," aligned with cardiac patients’ specific needs. In addition, this study delves into the intricate dynamics between different food items, exploring interactions such as the effects of combining protein- and carbohydrate-rich diets. This exploration serves as a focal point for in-depth data mining, offering nuanced perspectives on dietary patterns and their impact on heart health. The method used central to the diet recommendation system is the implementation of the Neural Random Forest algorithm, which serves as the cornerstone for generating tailored dietary suggestions. To ensure the system’s robustness and accuracy, a comparative assessment involving other prominent ML algorithms—namely Random Forest, Naïve Bayes, Support Vector Machine, and Decision Tree, was conducted. The results of this analysis underscore the superiority of the proposed -based system, demonstrating higher overall accuracy in delivering precise dietary recommendations compared with its counterparts. In conclusion, this study introduces an advanced diet recommendation system using ML, with the potential to notably reduce cardiac disease risk. By providing evidence-based dietary guidance, the system benefits both healthcare professionals and patients, showcasing the transformative capacity of ML in healthcare. This study underscores the significance of meticulous data analysis in refining dietary decisions for individuals with cardiac conditions.